Machine learning-based comparison of factors influencing estimated glomerular filtration rate in Chinese women with or without non-alcoholic fatty liver

Table 6 The average of the importance of risk factors derived from stochastic gradient boosting, random forest and extreme gradient boost, in NAFLD- (Model 2, excluding age)

Variables	SGB	XGBoost	Elasticnet	Average	Rank
Income	2.49	1.95	1.61	2.02
Body fat	7.57	2.68	1.48	3.91
Systolic blood pressure	28.66	30.68	0.59	19.98	6
Diastolic blood pressure	18.44	21.96	1.73	14.04
Leukocyte	9.07	5.26	6.63	6.99
Hemoglobin	12.51	1.95	3.14	5.87
Platelets	12.13	8.68	0.23	7.01
Fasting plasma glucose	6.67	4.96	0.7	4.11
Total bilirubin	9.07	5.16	3.37	5.87
Albumin	21.95	20.16	100	47.37	4
Globulin	1.32	0	0	0.44
Alkaline Phosphatase	2.75	0	0.02	0.92
Serum glutamic oxaloacetic transaminase	4.06	3.15	1.59	2.93
Serum glutamic pyruvic transaminase	9.09	6.48	1.74	5.77
Serum γ-glutamyl transpeptidase	1.11	0	0.11	0.41
Lactate dehydrogenase	100	100	0.63	66.88	1
Uric acid	66.92	63.24	36.68	55.61	2
Triglyceride	12.39	8	0.34	6.91
High density lipoprotein cholesterol	2.64	0.67	0.17	1.16
Low density lipoprotein cholesterol	14.18	10.11	0.51	8.27
Calcium	3.82	2.4	21.04	9.09
Phosphorus	5.65	6.52	0.1	4.09
Thyroid stimulating hormone	34.32	24.16	2.56	20.35	5
C-reactive protein	2.68	0	0	0.89
Forced expiratory volume in one second	61.02	64.4	26.93	50.78	3
Drink area	1.01	1.21	0	0.74
Smoke area	2.24	1.22	0.85	1.44
Betel nut area	0	0	0	0.00
Sport area	13.32	11.44	2.46	9.07
Sleep time	0.79	0.47	10.69	3.98
Marriage	8.88	7.85	30.55	15.76

SGB: Stochastic gradient boosting; XGBoost: eXtreme gradient boosting; NAFLD: Non-alcoholic fatty liver.